A presentation by Julie Collins, Senior Associate Scientist, AKADEMIYA2063

1. COVID-19 Vulnerability Hot Spots: Central Africa
Better preparedness through early identification and targeting
of the most exposed communities
Julie Collins
Senior Associate Scientist, AKADEMIYA2063
AKADEMIYA2063 Webinar Series on COVID-19
November 19, 2020

2. Outline
Motivation
Methodology
Central Africa vulnerability analysis
Enterprise-level analysis, DRC
Future directions
Conclusions
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3. Motivation
• Effects of crises are not geographically uniform
• Both the spread of COVID-19 and the ability to respond to its effects
vary between and within countries
• The severity of impacts on people’s livelihoods and food security
depends in part on existing patterns of vulnerability
• Objective: Identify areas within countries and regions that show the
highest levels of vulnerability to negative impacts of COVID
• These areas can be monitored closely and interventions prepared
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4. Methodology
Vulnerability:
• Propensity of a district to be exposed to spread of COVID-19;
• Limited capacity to control the pandemic and care for
infected people;
• Households’ exposure to negative food security impacts
1) Consider various factors shaping vulnerability
 Food security status
 Nutrition status
 Disease prevalence
 Access to health services
 Density
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5. Methodology
2) Build composite indicator to create typology of vulnerability
• Assign each area a value for each indicator:
𝐼 > 𝐼 𝑘 + 0.67 ∗ 𝑠𝑡𝑑(𝐼 𝑘) ∶ 3 = 𝑀𝑢𝑐ℎ 𝑚𝑜𝑟𝑒 𝑣𝑢𝑙𝑛𝑒𝑟𝑎𝑏𝑙𝑒
𝐼 𝑘 + 0.67 ∗ 𝑠𝑡𝑑(𝐼 𝑘) ≤ 𝐼 < 𝐼 𝑘 ∶ 2 = 𝑀𝑜𝑟𝑒 𝑣𝑢𝑙𝑛𝑒𝑟𝑎𝑏𝑙𝑒
𝐼 𝑘 ≤ 𝐼 < 𝐼 𝑘 − 0.67 ∗ 𝑠𝑡𝑑(𝐼 𝑘) ∶ 1 = 𝐿𝑒𝑠𝑠 𝑣𝑢𝑙𝑛𝑒𝑟𝑎𝑏𝑙𝑒
𝐼 < 𝐼 𝑘 − 0.67 ∗ 𝑠𝑡𝑑(𝐼 𝑘) ∶ 0 = 𝑀𝑢𝑐ℎ 𝑙𝑒𝑠𝑠 𝑣𝑢𝑙𝑛𝑒𝑟𝑎𝑏𝑙𝑒
• Calculate the composite vulnerability index for each area by summing up all the indicators
as follows:
Vulindex𝑖𝑗 = K −
𝑘
𝑤𝑙𝑖𝑗𝑘
Where K represents the number of indicators included in the composite index, w𝑙𝑖𝑗𝑘 the weight associated with
the rank l (0, 1, 2, 3) of the 𝑘 𝑡ℎ indicator of area j in country i.
w𝑙𝑖𝑗𝑘 =
k − 𝑙
k
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6. Indicators and data sources
Factor Indicator Source
Nutrition status Stunting
Demographic and Health Surveys (DHS)Access to health
services
Proportion of women (15-49) for whom
distance to health facilities is a big problem
Proportion of women (15-49) getting
assistance during childbirth from doctor,
nurse/midwife etc.
Disease prevalence
Prevalence of diabetes DHS or statistical year book
Prevalence of high blood pressure DHS or statistical year book
Food security status Share of food in total expenditure National surveys
Overcrowding Population density of inhabited areas
Center for International Earth Science
Information, Columbia University
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7. Results for Central Africa
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8. Food and nutrition security:
Stunting
• Significant variation within
countries
• Especially high rates in
central and eastern DRC,
northern Chad
• Pockets of higher
vulnerability in Cameroon
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9. Food and nutrition security:
Share of food in total
expenditure
• More within-region than
within-country variation
• Especially high vulnerability
in DRC
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10. Access to healthcare
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11. Disease burden
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12. Population density
• Measured as density of
inhabited areas
• DRC and some areas of
Chad have the highest
density
• Cameroon is relatively less
vulnerable in terms of
density
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13. Composite vulnerability index
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• All countries have regions
of much higher and much
lower vulnerability

14. Effects on firms in DRC
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15. Data
• 103 firms surveyed in April-May 2020 by the Fédération des
Entreprises du Congo (FEC)
• Over 60 percent of surveyed firms are in Kinshasa, followed by North
Kivu (16%)
62%
16%
6%
5%
5%
4% 2% 1%
Firm location
Kinshasa Nord Kivu Haut Katanga Lualaba Sud Kivu Maniema Ituri Kasai
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16. Enterprise size and sector
• Strong representation of enterprises in the service sector
• Firms range from micro to very large, with distinct sectoral patterns
0% 20% 40% 60% 80% 100%
Banks, financial services
Mining
Manufacturing
Energy
Construction
Agriculture, agro-processing
Health, medical, pharmacy
IT, digital, telecommunications
Hospitality and tourism
General trade / transport
Misc. services
Overall
Number of employees by sector
Less than 10 10 to 50 50 to 100 100 to 250 250 to 500 More than 500
18%
15%
13%
10%
9%
8%
8%
7%
6%
5%
3%
Firm sector (percent)
General trade / transport Misc. services
Hospitality and tourism IT, digital, telecommunications
Manufacturing Agriculture, agro-processing
Health, medical, pharmacy Energy
Banks, financial services Mining
Construction
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17. Effects of COVID-19 on firms
• Large shares of firms reduced activities; many firms could not pay
debts, salaries or taxes
• A handful of firms reported no effect or increased sales
How did COVID-19 affect your activities?
Response Percent of firms
No contracts signed or orders received from clients 42
Inability to pay debts to suppliers and banks 38
Inability to pay salaries 38
Less than 50% decrease in activities 36
More than 50% decrease in activities 34
Inability to pay taxes and fees 34
Cessation of activities 20
Increase in unsold stocks 19
Impossibility of importing raw materials 17
Inability to meet demand due to reduced activities 16
Suspension of work contracts 16
No impact on our activities 6
Increase in sales 2
Note: Percentages sum to over 100 due to multiple
responses
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18. Firms’ responses to COVID-19
• Over one third of firms instituted telework
• Many firms suspended investments, reduced production, and / or
laid off staff
Note: Percentages sum to over 100 due to multiple
responses
What measures have you taken during the COVID-19 crisis?
Response Percent of firms
Adoption of telework by some staff 37
Suspension of investments 36
Layoff / suspension of contracts for a portion of staff 33
Reduction in production 31
Cancellation of orders from suppliers 19
Reduction in imports 19
Cancellation of orders received from clients 17
Termination / renegotiation of leases 16
Sale of assets to meet commitments 9
Shifting to other activities 8
Increase in imports of finished products 4
Increase in imports of raw materials 3
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19. Effects on employment
• Less than half of all firms retained all employees
• Hospitality and tourism firms were the least likely to retain all
personnel and the most likely to close
0% 20% 40% 60% 80% 100%
Hospitality and tourism
Energy
General trade / transport
Manufacturing
IT, digital, telecommunications
Misc. services
Agriculture, agro-processing
Banks, financial services
Health, medical, pharmacy
Construction
Mining
Overall
Did you retain your personnel during the COVID-19 period?
Yes, all Yes, more than 50% Yes, half Yes, less than 50% No, firm closed
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20. Effects on financial standing
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Hospitality and tourism
General trade / transport
Manufacturing
Energy
IT, digital, telecommunications
Health, medical, pharmacy
Banks, financial services
Misc. services
Agriculture, agro-processing
Mining
Construction
Overall
What are your estimated losses due to COVID-19?
Less than 10% of annual revenues received in 2019
Between 10% and 50% of annual revenues received in 2019
Between 50% and 75% of annual revenues received in 2019
More than 75% of annual revenues received in 2019
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21. Effects by firm size
• Firms of all sizes suffered losses, but the largest firms were more
able to retain employees and avoid large financial losses
0% 20% 40% 60% 80% 100%
More than 250 employees
100 to 250 employees
50 to 100 employees
10 to 50 employees
Less than 10 employees
Did you retain your personnel during the COVID-
19 period?
Yes, all Yes, more than 50% Yes, half Yes, less than 50% No, firm closed
0% 20% 40% 60% 80% 100%
More than 250
100 to 250 employees
50 to 100 employees
10 to 50 employees
Less than 10 employees
What are your estimated losses due to
COVID-19?
Less than 10% of annual revenues received in 2019
Between 10% and 50% of annual revenues received in 2019
Between 50% and 75% of annual revenues received in 2019
More than 75% of annual revenues received in 2019
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22. Desired support from governments
• Three-fourths of firms would like to see tax reductions in response to
COVID-19
What measures would you like the government to take to
restart your activities?
Response
Percent of
firms
Reduce taxes 75
Create a support fund for businesses affected
by COVID-19 61
Subsidize businesses affected by COVID-19 55
Facilitate access to bank credit 43
Note: Percentages sum to over 100 due to multiple responses
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23. Future directions for vulnerability analysis
1) Explore other data sources and indicators
2) Construct indicators on micronutrient deficiencies
3) Examine changes in micronutrient consumption resulting
from COVID-related food price changes
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24. Conclusion
• Responses to COVID-19 need to prioritize most severely
affected areas
• In the absence of data on actual impacts, important to assess
likely hotspots early
• Areas with high levels of chronic vulnerability may be hardest
hit by COVID-19 and its effects on food security
• Employees of small firms and firms in exposed sectors are
particularly vulnerable
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25. THANK YOU
AKADEMIYA2063 – Kicukiro / Niboye KK 360 St 8 I P.O. Box 4729 Kigali-Rwanda
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26. Calories: Daily
intake per adult
equivalent
(2012)
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27. Iron: Daily
intake per adult
equivalent
(2012)
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